List models | Labric | Documentation

[
  {
    "id": "6b1e4c2a-9d3f-4a7b-8c5e-1f2a3b4c5d6e",
    "name": "Coating adhesion classifier",
    "task_type": "tabular",
    "description": "Predicts whether a cured coating passes the pull-off adhesion test from its cure schedule and substrate preparation.",
    "target_column": "adhesion_pass",
    "feature_columns": [
      "cure_temperature_c",
      "cure_time_min",
      "substrate_roughness_um",
      "primer_thickness_um"
    ],
    "image_columns": null,
    "problem_type": "binary",
    "currently_active": true,
    "status": "ready",
    "version_number": 3,
    "dataset_id": "2c7d9e1f-3a4b-4c5d-8e6f-7a8b9c0d1e2f",
    "quality_preset": "good_quality",
    "error_message": null,
    "training_started_at": "2026-08-14T09:12:04Z",
    "training_completed_at": "2026-08-14T09:41:37Z",
    "evaluation_metrics": {
      "accuracy": 0.94,
      "f1": 0.93,
      "precision": 0.92,
      "recall": 0.95,
      "roc_auc": 0.97
    }
  },
  {
    "id": "9f8e7d6c-5b4a-4c3d-9e2f-1a0b9c8d7e6f",
    "name": "Tensile strength regressor",
    "task_type": "tabular",
    "description": "Estimates tensile strength of extruded polymer samples from formulation and process parameters.",
    "target_column": "tensile_strength_mpa",
    "feature_columns": [
      "polymer_fraction",
      "anneal_temperature_c",
      "extrusion_speed_mm_s"
    ],
    "image_columns": null,
    "problem_type": "regression",
    "currently_active": false,
    "status": "training",
    "version_number": 1,
    "dataset_id": "4e5f6a7b-8c9d-4e0f-a1b2-c3d4e5f6a7b8",
    "quality_preset": "high_quality",
    "error_message": null,
    "training_started_at": "2026-08-21T15:03:11Z",
    "training_completed_at": null,
    "evaluation_metrics": null
  },
  {
    "id": "3a2b1c0d-9e8f-4a7b-b6c5-d4e3f2a1b0c9",
    "name": "Grain boundary segmentation",
    "task_type": "segmentation",
    "description": "Segments grain boundaries in optical micrographs of etched alloy cross-sections.",
    "target_column": "grain_boundary",
    "feature_columns": null,
    "image_columns": [
      "micrograph"
    ],
    "problem_type": "semantic_segmentation",
    "currently_active": true,
    "status": "ready",
    "version_number": 2,
    "dataset_id": "7b8c9d0e-1f2a-4b3c-8d4e-5f6a7b8c9d0e",
    "quality_preset": "best_quality",
    "error_message": null,
    "training_started_at": "2026-08-02T11:20:45Z",
    "training_completed_at": "2026-08-02T13:58:02Z",
    "evaluation_metrics": {
      "dice": 0.89,
      "iou": 0.81
    }
  }
]

List the organization’s ML models, newest first.

Returns every non-archived model with its task type, target and feature columns, training status, and evaluation metrics. The status describes the newest version. A model serves predictions only while currently_active is true; a retrain stops it serving until the new version is ready.

Requires an API key with the read scope.

Authentication

AuthorizationBearer

Bearer authentication of the form Bearer <token>, where token is your auth token.

Response

idstringformat: "uuid"

namestring

task_typestring

descriptionstring or nullOptional

target_columnstring or nullOptional

feature_columnslist of strings or nullOptional

image_columnslist of strings or nullOptional

problem_typestring or nullOptional

currently_activebooleanOptionalDefaults to false

statusstring or nullOptional

version_numberinteger or nullOptional

dataset_idstring or nullOptionalformat: "uuid"

quality_presetstring or nullOptional

error_messagestring or nullOptional

training_started_atdatetime or nullOptional

training_completed_atdatetime or nullOptional

evaluation_metricsmap from strings to any or nullOptional

Errors

400

Bad Request Error

401

Unauthorized Error

403

Forbidden Error

404

Not Found Error

422

Unprocessable Entity Error

500

Internal Server Error